Overview

Dataset statistics

Number of variables19
Number of observations36275
Missing cells12929
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 MiB
Average record size in memory152.0 B

Variable types

Text1
Categorical8
Numeric10

Alerts

no_of_adults is highly imbalanced (52.4%)Imbalance
required_car_parking_space is highly imbalanced (80.2%)Imbalance
room_type_reserved is highly imbalanced (62.6%)Imbalance
repeated_guest is highly imbalanced (82.8%)Imbalance
no_of_adults has 413 (1.1%) missing valuesMissing
no_of_weekend_nights has 367 (1.0%) missing valuesMissing
no_of_week_nights has 807 (2.2%) missing valuesMissing
type_of_meal_plan has 526 (1.5%) missing valuesMissing
required_car_parking_space has 2592 (7.1%) missing valuesMissing
room_type_reserved has 1171 (3.2%) missing valuesMissing
lead_time has 472 (1.3%) missing valuesMissing
arrival_year has 378 (1.0%) missing valuesMissing
arrival_month has 504 (1.4%) missing valuesMissing
arrival_date has 981 (2.7%) missing valuesMissing
market_segment_type has 1512 (4.2%) missing valuesMissing
repeated_guest has 586 (1.6%) missing valuesMissing
no_of_previous_cancellations has 497 (1.4%) missing valuesMissing
no_of_previous_bookings_not_canceled has 550 (1.5%) missing valuesMissing
avg_price_per_room has 460 (1.3%) missing valuesMissing
no_of_special_requests has 789 (2.2%) missing valuesMissing
no_of_previous_cancellations is highly skewed (γ1 = 25.03312517)Skewed
Booking_ID has unique valuesUnique
no_of_children has 33275 (91.7%) zerosZeros
no_of_weekend_nights has 16715 (46.1%) zerosZeros
no_of_week_nights has 2327 (6.4%) zerosZeros
lead_time has 1277 (3.5%) zerosZeros
no_of_previous_cancellations has 35441 (97.7%) zerosZeros
no_of_previous_bookings_not_canceled has 34923 (96.3%) zerosZeros
avg_price_per_room has 539 (1.5%) zerosZeros
no_of_special_requests has 19350 (53.3%) zerosZeros

Reproduction

Analysis started2024-05-10 12:17:12.115002
Analysis finished2024-05-10 12:17:17.517100
Duration5.4 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Booking_ID
Text

UNIQUE 

Distinct36275
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size283.5 KiB
2024-05-10T15:17:17.632035image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters290200
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36275 ?
Unique (%)100.0%

Sample

1st rowINN00001
2nd rowINN00002
3rd rowINN00003
4th rowINN00004
5th rowINN00005
ValueCountFrequency (%)
inn00001 1
 
< 0.1%
inn00007 1
 
< 0.1%
inn00070 1
 
< 0.1%
inn00009 1
 
< 0.1%
inn00003 1
 
< 0.1%
inn00004 1
 
< 0.1%
inn00005 1
 
< 0.1%
inn00006 1
 
< 0.1%
inn00008 1
 
< 0.1%
inn00020 1
 
< 0.1%
Other values (36265) 36265
> 99.9%
2024-05-10T15:17:17.796997image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 72550
25.0%
I 36275
12.5%
1 24958
 
8.6%
0 24953
 
8.6%
2 24934
 
8.6%
3 21134
 
7.3%
4 14858
 
5.1%
5 14858
 
5.1%
6 14133
 
4.9%
7 13853
 
4.8%
Other values (2) 27694
 
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 290200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 72550
25.0%
I 36275
12.5%
1 24958
 
8.6%
0 24953
 
8.6%
2 24934
 
8.6%
3 21134
 
7.3%
4 14858
 
5.1%
5 14858
 
5.1%
6 14133
 
4.9%
7 13853
 
4.8%
Other values (2) 27694
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 290200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 72550
25.0%
I 36275
12.5%
1 24958
 
8.6%
0 24953
 
8.6%
2 24934
 
8.6%
3 21134
 
7.3%
4 14858
 
5.1%
5 14858
 
5.1%
6 14133
 
4.9%
7 13853
 
4.8%
Other values (2) 27694
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 290200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 72550
25.0%
I 36275
12.5%
1 24958
 
8.6%
0 24953
 
8.6%
2 24934
 
8.6%
3 21134
 
7.3%
4 14858
 
5.1%
5 14858
 
5.1%
6 14133
 
4.9%
7 13853
 
4.8%
Other values (2) 27694
 
9.5%

no_of_adults
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing413
Missing (%)1.1%
Memory size283.5 KiB
2.0
25813 
1.0
7606 
3.0
 
2290
0.0
 
137
4.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters107586
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 25813
71.2%
1.0 7606
 
21.0%
3.0 2290
 
6.3%
0.0 137
 
0.4%
4.0 16
 
< 0.1%
(Missing) 413
 
1.1%

Length

2024-05-10T15:17:17.869008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T15:17:17.918896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 25813
72.0%
1.0 7606
 
21.2%
3.0 2290
 
6.4%
0.0 137
 
0.4%
4.0 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 35999
33.5%
. 35862
33.3%
2 25813
24.0%
1 7606
 
7.1%
3 2290
 
2.1%
4 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35999
33.5%
. 35862
33.3%
2 25813
24.0%
1 7606
 
7.1%
3 2290
 
2.1%
4 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35999
33.5%
. 35862
33.3%
2 25813
24.0%
1 7606
 
7.1%
3 2290
 
2.1%
4 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35999
33.5%
. 35862
33.3%
2 25813
24.0%
1 7606
 
7.1%
3 2290
 
2.1%
4 16
 
< 0.1%

no_of_children
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing324
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean0.10536564
Minimum0
Maximum10
Zeros33275
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2024-05-10T15:17:17.963104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4028713
Coefficient of variation (CV)3.8235549
Kurtosis37.117523
Mean0.10536564
Median Absolute Deviation (MAD)0
Skewness4.7144081
Sum3788
Variance0.16230528
MonotonicityNot monotonic
2024-05-10T15:17:18.007605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 33275
91.7%
1 1605
 
4.4%
2 1049
 
2.9%
3 19
 
0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
(Missing) 324
 
0.9%
ValueCountFrequency (%)
0 33275
91.7%
1 1605
 
4.4%
2 1049
 
2.9%
3 19
 
0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 2
 
< 0.1%
3 19
 
0.1%
2 1049
 
2.9%
1 1605
 
4.4%
0 33275
91.7%

no_of_weekend_nights
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing367
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.81020942
Minimum0
Maximum7
Zeros16715
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2024-05-10T15:17:18.046274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.87085698
Coefficient of variation (CV)1.0748542
Kurtosis0.31186403
Mean0.81020942
Median Absolute Deviation (MAD)1
Skewness0.74090195
Sum29093
Variance0.75839188
MonotonicityNot monotonic
2024-05-10T15:17:18.085522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 16715
46.1%
1 9888
27.3%
2 8970
24.7%
3 152
 
0.4%
4 128
 
0.4%
5 34
 
0.1%
6 20
 
0.1%
7 1
 
< 0.1%
(Missing) 367
 
1.0%
ValueCountFrequency (%)
0 16715
46.1%
1 9888
27.3%
2 8970
24.7%
3 152
 
0.4%
4 128
 
0.4%
5 34
 
0.1%
6 20
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 20
 
0.1%
5 34
 
0.1%
4 128
 
0.4%
3 152
 
0.4%
2 8970
24.7%
1 9888
27.3%
0 16715
46.1%

no_of_week_nights
Real number (ℝ)

MISSING  ZEROS 

Distinct18
Distinct (%)0.1%
Missing807
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.20331
Minimum0
Maximum17
Zeros2327
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2024-05-10T15:17:18.127251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4098899
Coefficient of variation (CV)0.63989629
Kurtosis7.8816893
Mean2.20331
Median Absolute Deviation (MAD)1
Skewness1.604805
Sum78147
Variance1.9877895
MonotonicityNot monotonic
2024-05-10T15:17:18.169338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 11191
30.9%
1 9295
25.6%
3 7660
21.1%
4 2914
 
8.0%
0 2327
 
6.4%
5 1584
 
4.4%
6 184
 
0.5%
7 109
 
0.3%
8 61
 
0.2%
10 58
 
0.2%
Other values (8) 85
 
0.2%
(Missing) 807
 
2.2%
ValueCountFrequency (%)
0 2327
 
6.4%
1 9295
25.6%
2 11191
30.9%
3 7660
21.1%
4 2914
 
8.0%
5 1584
 
4.4%
6 184
 
0.5%
7 109
 
0.3%
8 61
 
0.2%
9 32
 
0.1%
ValueCountFrequency (%)
17 3
 
< 0.1%
16 2
 
< 0.1%
15 10
 
< 0.1%
14 7
 
< 0.1%
13 5
 
< 0.1%
12 9
 
< 0.1%
11 17
 
< 0.1%
10 58
0.2%
9 32
0.1%
8 61
0.2%

type_of_meal_plan
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing526
Missing (%)1.5%
Memory size283.5 KiB
Meal Plan 1
27421 
Not Selected
5057 
Meal Plan 2
3266 
Meal Plan 3
 
5

Length

Max length12
Median length11
Mean length11.141459
Min length11

Characters and Unicode

Total characters398296
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Selected
2nd rowMeal Plan 1
3rd rowMeal Plan 1
4th rowNot Selected
5th rowMeal Plan 2

Common Values

ValueCountFrequency (%)
Meal Plan 1 27421
75.6%
Not Selected 5057
 
13.9%
Meal Plan 2 3266
 
9.0%
Meal Plan 3 5
 
< 0.1%
(Missing) 526
 
1.5%

Length

2024-05-10T15:17:18.216698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T15:17:18.256574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
meal 30692
30.0%
plan 30692
30.0%
1 27421
26.8%
not 5057
 
4.9%
selected 5057
 
4.9%
2 3266
 
3.2%
3 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 66441
16.7%
66441
16.7%
a 61384
15.4%
e 45863
11.5%
M 30692
7.7%
P 30692
7.7%
n 30692
7.7%
1 27421
6.9%
t 10114
 
2.5%
N 5057
 
1.3%
Other values (6) 23499
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 398296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 66441
16.7%
66441
16.7%
a 61384
15.4%
e 45863
11.5%
M 30692
7.7%
P 30692
7.7%
n 30692
7.7%
1 27421
6.9%
t 10114
 
2.5%
N 5057
 
1.3%
Other values (6) 23499
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 398296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 66441
16.7%
66441
16.7%
a 61384
15.4%
e 45863
11.5%
M 30692
7.7%
P 30692
7.7%
n 30692
7.7%
1 27421
6.9%
t 10114
 
2.5%
N 5057
 
1.3%
Other values (6) 23499
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 398296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 66441
16.7%
66441
16.7%
a 61384
15.4%
e 45863
11.5%
M 30692
7.7%
P 30692
7.7%
n 30692
7.7%
1 27421
6.9%
t 10114
 
2.5%
N 5057
 
1.3%
Other values (6) 23499
 
5.9%

required_car_parking_space
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2592
Missing (%)7.1%
Memory size283.5 KiB
0.0
32649 
1.0
 
1034

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters101049
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 32649
90.0%
1.0 1034
 
2.9%
(Missing) 2592
 
7.1%

Length

2024-05-10T15:17:18.303926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T15:17:18.341074image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 32649
96.9%
1.0 1034
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 66332
65.6%
. 33683
33.3%
1 1034
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101049
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 66332
65.6%
. 33683
33.3%
1 1034
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101049
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 66332
65.6%
. 33683
33.3%
1 1034
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101049
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 66332
65.6%
. 33683
33.3%
1 1034
 
1.0%

room_type_reserved
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing1171
Missing (%)3.2%
Memory size283.5 KiB
Room_Type 1
27234 
Room_Type 4
5851 
Room_Type 6
 
939
Room_Type 2
 
664
Room_Type 5
 
256
Other values (2)
 
160

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters386144
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoom_Type 1
2nd rowRoom_Type 1
3rd rowRoom_Type 1
4th rowRoom_Type 1
5th rowRoom_Type 1

Common Values

ValueCountFrequency (%)
Room_Type 1 27234
75.1%
Room_Type 4 5851
 
16.1%
Room_Type 6 939
 
2.6%
Room_Type 2 664
 
1.8%
Room_Type 5 256
 
0.7%
Room_Type 7 154
 
0.4%
Room_Type 3 6
 
< 0.1%
(Missing) 1171
 
3.2%

Length

2024-05-10T15:17:18.380726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T15:17:18.423711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
room_type 35104
50.0%
1 27234
38.8%
4 5851
 
8.3%
6 939
 
1.3%
2 664
 
0.9%
5 256
 
0.4%
7 154
 
0.2%
3 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 70208
18.2%
R 35104
9.1%
m 35104
9.1%
_ 35104
9.1%
T 35104
9.1%
y 35104
9.1%
p 35104
9.1%
e 35104
9.1%
35104
9.1%
1 27234
 
7.1%
Other values (6) 7870
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 386144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 70208
18.2%
R 35104
9.1%
m 35104
9.1%
_ 35104
9.1%
T 35104
9.1%
y 35104
9.1%
p 35104
9.1%
e 35104
9.1%
35104
9.1%
1 27234
 
7.1%
Other values (6) 7870
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 386144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 70208
18.2%
R 35104
9.1%
m 35104
9.1%
_ 35104
9.1%
T 35104
9.1%
y 35104
9.1%
p 35104
9.1%
e 35104
9.1%
35104
9.1%
1 27234
 
7.1%
Other values (6) 7870
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 386144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 70208
18.2%
R 35104
9.1%
m 35104
9.1%
_ 35104
9.1%
T 35104
9.1%
y 35104
9.1%
p 35104
9.1%
e 35104
9.1%
35104
9.1%
1 27234
 
7.1%
Other values (6) 7870
 
2.0%

lead_time
Real number (ℝ)

MISSING  ZEROS 

Distinct352
Distinct (%)1.0%
Missing472
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean85.276569
Minimum0
Maximum443
Zeros1277
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2024-05-10T15:17:18.479966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q117
median57
Q3126
95-th percentile273
Maximum443
Range443
Interquartile range (IQR)109

Descriptive statistics

Standard deviation85.998845
Coefficient of variation (CV)1.0084698
Kurtosis1.1780846
Mean85.276569
Median Absolute Deviation (MAD)47
Skewness1.29236
Sum3053157
Variance7395.8013
MonotonicityNot monotonic
2024-05-10T15:17:18.532852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1277
 
3.5%
1 1068
 
2.9%
2 631
 
1.7%
3 622
 
1.7%
4 620
 
1.7%
5 574
 
1.6%
6 517
 
1.4%
8 433
 
1.2%
7 421
 
1.2%
12 404
 
1.1%
Other values (342) 29236
80.6%
(Missing) 472
 
1.3%
ValueCountFrequency (%)
0 1277
3.5%
1 1068
2.9%
2 631
1.7%
3 622
1.7%
4 620
1.7%
5 574
1.6%
6 517
1.4%
7 421
 
1.2%
8 433
 
1.2%
9 330
 
0.9%
ValueCountFrequency (%)
443 22
 
0.1%
433 20
 
0.1%
418 59
0.2%
386 69
0.2%
381 2
 
< 0.1%
377 68
0.2%
372 1
 
< 0.1%
361 5
 
< 0.1%
359 16
 
< 0.1%
355 1
 
< 0.1%

arrival_year
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing378
Missing (%)1.0%
Memory size283.5 KiB
2018.0
29451 
2017.0
6446 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters215382
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018.0
2nd row2018.0
3rd row2018.0
4th row2018.0
5th row2018.0

Common Values

ValueCountFrequency (%)
2018.0 29451
81.2%
2017.0 6446
 
17.8%
(Missing) 378
 
1.0%

Length

2024-05-10T15:17:18.580395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T15:17:18.617400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
2018.0 29451
82.0%
2017.0 6446
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 71794
33.3%
2 35897
16.7%
1 35897
16.7%
. 35897
16.7%
8 29451
13.7%
7 6446
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 215382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 71794
33.3%
2 35897
16.7%
1 35897
16.7%
. 35897
16.7%
8 29451
13.7%
7 6446
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 215382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 71794
33.3%
2 35897
16.7%
1 35897
16.7%
. 35897
16.7%
8 29451
13.7%
7 6446
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 215382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 71794
33.3%
2 35897
16.7%
1 35897
16.7%
. 35897
16.7%
8 29451
13.7%
7 6446
 
3.0%

arrival_month
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing504
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean7.4240306
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2024-05-10T15:17:18.653338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0682767
Coefficient of variation (CV)0.41328988
Kurtosis-0.93196042
Mean7.4240306
Median Absolute Deviation (MAD)2
Skewness-0.34793619
Sum265565
Variance9.4143221
MonotonicityNot monotonic
2024-05-10T15:17:18.692010image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 5238
14.4%
9 4550
12.5%
8 3761
10.4%
6 3162
8.7%
12 2977
8.2%
11 2937
8.1%
7 2887
8.0%
4 2700
7.4%
5 2563
7.1%
3 2328
6.4%
Other values (2) 2668
7.4%
ValueCountFrequency (%)
1 1000
 
2.8%
2 1668
 
4.6%
3 2328
6.4%
4 2700
7.4%
5 2563
7.1%
6 3162
8.7%
7 2887
8.0%
8 3761
10.4%
9 4550
12.5%
10 5238
14.4%
ValueCountFrequency (%)
12 2977
8.2%
11 2937
8.1%
10 5238
14.4%
9 4550
12.5%
8 3761
10.4%
7 2887
8.0%
6 3162
8.7%
5 2563
7.1%
4 2700
7.4%
3 2328
6.4%

arrival_date
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)0.1%
Missing981
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean15.605712
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2024-05-10T15:17:18.733951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7434836
Coefficient of variation (CV)0.56027457
Kurtosis-1.157733
Mean15.605712
Median Absolute Deviation (MAD)8
Skewness0.027554339
Sum550788
Variance76.448505
MonotonicityNot monotonic
2024-05-10T15:17:18.779132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13 1321
 
3.6%
17 1314
 
3.6%
2 1292
 
3.6%
4 1287
 
3.5%
19 1286
 
3.5%
16 1270
 
3.5%
20 1243
 
3.4%
15 1238
 
3.4%
18 1232
 
3.4%
6 1231
 
3.4%
Other values (21) 22580
62.2%
ValueCountFrequency (%)
1 1104
3.0%
2 1292
3.6%
3 1076
3.0%
4 1287
3.5%
5 1119
3.1%
6 1231
3.4%
7 1077
3.0%
8 1166
3.2%
9 1103
3.0%
10 1062
2.9%
ValueCountFrequency (%)
31 565
1.6%
30 1178
3.2%
29 1167
3.2%
28 1109
3.1%
27 1031
2.8%
26 1117
3.1%
25 1111
3.1%
24 1074
3.0%
23 961
2.6%
22 997
2.7%

market_segment_type
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1512
Missing (%)4.2%
Memory size283.5 KiB
Online
22264 
Offline
10076 
Corporate
 
1926
Complementary
 
375
Aviation
 
122

Length

Max length13
Median length6
Mean length6.5385899
Min length6

Characters and Unicode

Total characters227301
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOnline
2nd rowOnline
3rd rowOnline
4th rowOnline
5th rowOnline

Common Values

ValueCountFrequency (%)
Online 22264
61.4%
Offline 10076
27.8%
Corporate 1926
 
5.3%
Complementary 375
 
1.0%
Aviation 122
 
0.3%
(Missing) 1512
 
4.2%

Length

2024-05-10T15:17:18.901111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T15:17:18.942049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
online 22264
64.0%
offline 10076
29.0%
corporate 1926
 
5.5%
complementary 375
 
1.1%
aviation 122
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n 55101
24.2%
e 35016
15.4%
l 32715
14.4%
i 32584
14.3%
O 32340
14.2%
f 20152
 
8.9%
o 4349
 
1.9%
r 4227
 
1.9%
a 2423
 
1.1%
t 2423
 
1.1%
Other values (6) 5971
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 227301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 55101
24.2%
e 35016
15.4%
l 32715
14.4%
i 32584
14.3%
O 32340
14.2%
f 20152
 
8.9%
o 4349
 
1.9%
r 4227
 
1.9%
a 2423
 
1.1%
t 2423
 
1.1%
Other values (6) 5971
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 227301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 55101
24.2%
e 35016
15.4%
l 32715
14.4%
i 32584
14.3%
O 32340
14.2%
f 20152
 
8.9%
o 4349
 
1.9%
r 4227
 
1.9%
a 2423
 
1.1%
t 2423
 
1.1%
Other values (6) 5971
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 227301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 55101
24.2%
e 35016
15.4%
l 32715
14.4%
i 32584
14.3%
O 32340
14.2%
f 20152
 
8.9%
o 4349
 
1.9%
r 4227
 
1.9%
a 2423
 
1.1%
t 2423
 
1.1%
Other values (6) 5971
 
2.6%

repeated_guest
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing586
Missing (%)1.6%
Memory size283.5 KiB
0.0
34773 
1.0
 
916

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters107067
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 34773
95.9%
1.0 916
 
2.5%
(Missing) 586
 
1.6%

Length

2024-05-10T15:17:18.987066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T15:17:19.023538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 34773
97.4%
1.0 916
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 70462
65.8%
. 35689
33.3%
1 916
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 107067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 70462
65.8%
. 35689
33.3%
1 916
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 107067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 70462
65.8%
. 35689
33.3%
1 916
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 107067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 70462
65.8%
. 35689
33.3%
1 916
 
0.9%

no_of_previous_cancellations
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing497
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean0.023645816
Minimum0
Maximum13
Zeros35441
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2024-05-10T15:17:19.057241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37083473
Coefficient of variation (CV)15.68289
Kurtosis722.93754
Mean0.023645816
Median Absolute Deviation (MAD)0
Skewness25.033125
Sum846
Variance0.1375184
MonotonicityNot monotonic
2024-05-10T15:17:19.099737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 35441
97.7%
1 197
 
0.5%
2 46
 
0.1%
3 43
 
0.1%
11 25
 
0.1%
5 11
 
< 0.1%
4 10
 
< 0.1%
13 4
 
< 0.1%
6 1
 
< 0.1%
(Missing) 497
 
1.4%
ValueCountFrequency (%)
0 35441
97.7%
1 197
 
0.5%
2 46
 
0.1%
3 43
 
0.1%
4 10
 
< 0.1%
5 11
 
< 0.1%
6 1
 
< 0.1%
11 25
 
0.1%
13 4
 
< 0.1%
ValueCountFrequency (%)
13 4
 
< 0.1%
11 25
 
0.1%
6 1
 
< 0.1%
5 11
 
< 0.1%
4 10
 
< 0.1%
3 43
 
0.1%
2 46
 
0.1%
1 197
 
0.5%
0 35441
97.7%

no_of_previous_bookings_not_canceled
Real number (ℝ)

MISSING  ZEROS 

Distinct59
Distinct (%)0.2%
Missing550
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean0.15445766
Minimum0
Maximum58
Zeros34923
Zeros (%)96.3%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2024-05-10T15:17:19.148050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum58
Range58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.764805
Coefficient of variation (CV)11.425817
Kurtosis453.13648
Mean0.15445766
Median Absolute Deviation (MAD)0
Skewness19.175712
Sum5518
Variance3.1145365
MonotonicityNot monotonic
2024-05-10T15:17:19.204012image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34923
96.3%
1 227
 
0.6%
2 109
 
0.3%
3 79
 
0.2%
4 64
 
0.2%
5 59
 
0.2%
6 36
 
0.1%
8 23
 
0.1%
7 22
 
0.1%
10 19
 
0.1%
Other values (49) 164
 
0.5%
(Missing) 550
 
1.5%
ValueCountFrequency (%)
0 34923
96.3%
1 227
 
0.6%
2 109
 
0.3%
3 79
 
0.2%
4 64
 
0.2%
5 59
 
0.2%
6 36
 
0.1%
7 22
 
0.1%
8 23
 
0.1%
9 19
 
0.1%
ValueCountFrequency (%)
58 1
< 0.1%
57 1
< 0.1%
56 1
< 0.1%
55 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%
52 1
< 0.1%
51 1
< 0.1%
50 1
< 0.1%
49 1
< 0.1%

avg_price_per_room
Real number (ℝ)

MISSING  ZEROS 

Distinct3905
Distinct (%)10.9%
Missing460
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean103.41821
Minimum0
Maximum540
Zeros539
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2024-05-10T15:17:19.258340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61
Q180.3
median99.45
Q3120
95-th percentile164.9
Maximum540
Range540
Interquartile range (IQR)39.7

Descriptive statistics

Standard deviation35.057342
Coefficient of variation (CV)0.33898617
Kurtosis3.1192587
Mean103.41821
Median Absolute Deviation (MAD)20.25
Skewness0.65625004
Sum3703923.1
Variance1229.0172
MonotonicityNot monotonic
2024-05-10T15:17:19.313126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 840
 
2.3%
75 815
 
2.2%
90 693
 
1.9%
115 658
 
1.8%
95 658
 
1.8%
120 604
 
1.7%
100 598
 
1.6%
110 553
 
1.5%
0 539
 
1.5%
85 501
 
1.4%
Other values (3895) 29356
80.9%
(Missing) 460
 
1.3%
ValueCountFrequency (%)
0 539
1.5%
0.5 1
 
< 0.1%
1 9
 
< 0.1%
1.48 1
 
< 0.1%
1.6 1
 
< 0.1%
2 6
 
< 0.1%
3 3
 
< 0.1%
4.5 1
 
< 0.1%
6 25
 
0.1%
6.5 1
 
< 0.1%
ValueCountFrequency (%)
540 1
 
< 0.1%
375.5 1
 
< 0.1%
365 1
 
< 0.1%
349.63 1
 
< 0.1%
316 1
 
< 0.1%
314.1 1
 
< 0.1%
306 2
 
< 0.1%
300 5
< 0.1%
299.33 1
 
< 0.1%
297 1
 
< 0.1%

no_of_special_requests
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing789
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean0.61934284
Minimum0
Maximum5
Zeros19350
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size283.5 KiB
2024-05-10T15:17:19.355995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78584926
Coefficient of variation (CV)1.2688437
Kurtosis0.88609123
Mean0.61934284
Median Absolute Deviation (MAD)0
Skewness1.1451999
Sum21978
Variance0.61755906
MonotonicityNot monotonic
2024-05-10T15:17:19.394033image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19350
53.3%
1 11125
30.7%
2 4273
 
11.8%
3 653
 
1.8%
4 77
 
0.2%
5 8
 
< 0.1%
(Missing) 789
 
2.2%
ValueCountFrequency (%)
0 19350
53.3%
1 11125
30.7%
2 4273
 
11.8%
3 653
 
1.8%
4 77
 
0.2%
5 8
 
< 0.1%
ValueCountFrequency (%)
5 8
 
< 0.1%
4 77
 
0.2%
3 653
 
1.8%
2 4273
 
11.8%
1 11125
30.7%
0 19350
53.3%

booking_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size283.5 KiB
Not_Canceled
24390 
Canceled
11885 

Length

Max length12
Median length12
Mean length10.689456
Min length8

Characters and Unicode

Total characters387760
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot_Canceled
2nd rowNot_Canceled
3rd rowCanceled
4th rowCanceled
5th rowCanceled

Common Values

ValueCountFrequency (%)
Not_Canceled 24390
67.2%
Canceled 11885
32.8%

Length

2024-05-10T15:17:19.440880image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T15:17:19.482661image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
not_canceled 24390
67.2%
canceled 11885
32.8%

Most occurring characters

ValueCountFrequency (%)
e 72550
18.7%
C 36275
9.4%
a 36275
9.4%
n 36275
9.4%
c 36275
9.4%
l 36275
9.4%
d 36275
9.4%
N 24390
 
6.3%
o 24390
 
6.3%
t 24390
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 387760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 72550
18.7%
C 36275
9.4%
a 36275
9.4%
n 36275
9.4%
c 36275
9.4%
l 36275
9.4%
d 36275
9.4%
N 24390
 
6.3%
o 24390
 
6.3%
t 24390
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 387760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 72550
18.7%
C 36275
9.4%
a 36275
9.4%
n 36275
9.4%
c 36275
9.4%
l 36275
9.4%
d 36275
9.4%
N 24390
 
6.3%
o 24390
 
6.3%
t 24390
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 387760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 72550
18.7%
C 36275
9.4%
a 36275
9.4%
n 36275
9.4%
c 36275
9.4%
l 36275
9.4%
d 36275
9.4%
N 24390
 
6.3%
o 24390
 
6.3%
t 24390
 
6.3%

Interactions

2024-05-10T15:17:16.598076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:12.827636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.317842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.774465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.180971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.562214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.941711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.391164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.802614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.201835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.641588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:12.894817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.357511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.815020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.218778image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.598187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.980572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.429991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.840883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.239551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.689496image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:12.948725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.397491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.856538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.257656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.637800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.020665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.471722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.883428image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.280775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.735520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.000466image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.441057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.898958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.298642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.678203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.062142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.512177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.925376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.322774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.774442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.067441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.478165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.937013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.332983image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.713065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.097653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.550455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.963626image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.358731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.813098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.126642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.516468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.976841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.371922image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.750030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.134400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.600864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.001963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.397183image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.851437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.165188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.557233image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.016082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.409395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.788374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.173171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.641470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.042533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.435512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.894446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.203984image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.656274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.057386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.448301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.826796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.211855image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.681605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.083369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.475400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.937914image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.242835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.697196image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.099330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.488007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.865734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.315229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.723989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.123232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.515929image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.978212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.280807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:13.735895image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.140415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.524004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:14.904876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.352810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:15.763231image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.162695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-10T15:17:16.557447image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-05-10T15:17:17.110728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T15:17:17.234546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-10T15:17:17.426001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Booking_IDno_of_adultsno_of_childrenno_of_weekend_nightsno_of_week_nightstype_of_meal_planrequired_car_parking_spaceroom_type_reservedlead_timearrival_yeararrival_montharrival_datemarket_segment_typerepeated_guestno_of_previous_cancellationsno_of_previous_bookings_not_canceledavg_price_per_roomno_of_special_requestsbooking_status
0INN00001NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNot_Canceled
1INN000022.00.02.03.0Not Selected0.0Room_Type 15.02018.011.06.0Online0.00.00.0106.681.0Not_Canceled
2INN000031.00.02.01.0Meal Plan 10.0Room_Type 11.02018.02.028.0Online0.00.00.060.000.0Canceled
3INN000042.00.00.02.0Meal Plan 10.0Room_Type 1211.02018.05.020.0Online0.00.00.0100.000.0Canceled
4INN000052.00.01.01.0Not Selected0.0Room_Type 148.02018.04.011.0Online0.00.00.094.500.0Canceled
5INN000062.00.00.02.0Meal Plan 20.0Room_Type 1346.02018.09.013.0Online0.00.00.0115.001.0Canceled
6INN000072.00.01.03.0Meal Plan 10.0Room_Type 134.02017.010.015.0Online0.00.00.0107.551.0Not_Canceled
7INN000082.00.01.03.0Meal Plan 10.0Room_Type 483.02018.012.026.0Online0.00.00.0105.611.0Not_Canceled
8INN000093.00.00.04.0Meal Plan 10.0Room_Type 1121.02018.07.06.0Offline0.00.00.096.901.0Not_Canceled
9INN000102.00.00.05.0Meal Plan 10.0Room_Type 444.02018.010.018.0Online0.00.00.0133.443.0Not_Canceled
Booking_IDno_of_adultsno_of_childrenno_of_weekend_nightsno_of_week_nightstype_of_meal_planrequired_car_parking_spaceroom_type_reservedlead_timearrival_yeararrival_montharrival_datemarket_segment_typerepeated_guestno_of_previous_cancellationsno_of_previous_bookings_not_canceledavg_price_per_roomno_of_special_requestsbooking_status
36265INN362662.00.01.03.0Meal Plan 10.0Room_Type 115.02018.05.030.0Online0.00.00.0100.730.0Not_Canceled
36266INN362672.00.02.02.0Meal Plan 10.0Room_Type 28.02018.03.04.0Online0.00.00.085.961.0Canceled
36267INN362682.00.01.00.0Not Selected0.0Room_Type 1NaN2018.07.011.0Online0.00.00.093.150.0Canceled
36268INN362691.00.00.03.0Meal Plan 10.0Room_Type 1166.02018.011.01.0Offline0.00.00.0110.000.0Canceled
36269INN362702.02.00.01.0Meal Plan 10.0Room_Type 60.02018.010.06.0Online0.00.00.0216.000.0Canceled
36270INN362713.00.02.0NaNMeal Plan 10.0NaN85.02018.08.03.0OnlineNaN0.00.0167.801.0Not_Canceled
36271INN362722.00.01.03.0Meal Plan 10.0Room_Type 1228.02018.010.017.0Online0.00.00.090.952.0Canceled
36272INN362732.00.02.06.0Meal Plan 10.0Room_Type 1148.02018.07.01.0Online0.00.00.098.392.0Not_Canceled
36273INN362742.00.00.03.0Not Selected0.0Room_Type 163.02018.04.021.0Online0.00.00.094.500.0Canceled
36274INN362752.00.01.02.0Meal Plan 1NaNRoom_Type 1207.02018.012.030.0Offline0.00.00.0161.670.0Not_Canceled